Yukyung Lee

Yukyung Lee

Postdoctoral Associate

Boston University (tinlab)

Professional Summary

I am a postdoctoral associate at Boston University, working with Prof. Najoung Kim and Prof. Sebastian Schuster. I received my Ph.D. from Korea University, advised by Prof. Pilsung Kang. During my Ph.D, I was research intern at NAVER and contributed to CLOVA for Writing. I completed my B.S. at HUFS, advised by Prof. Chungmok Lee

My research focuses on LLM evaluation, aiming to discover, measure, understand, and improve the capabilities of language models. My long-term research vision is to establish a science of evaluation for language models. I like to think about what makes evaluation reliable and what it truly tells us about these models. I am also interested in LLM agents that autonomously solve complex problems in research and engineering, and how to reliably evaluate them.

Education

PhD Industrial Management & Engineering

Korea University

BSc Industrial Management & Engineering, BA International Finance (Double Major)

HUFS

Interests

Language Model Evaluation LLM Agent Math/Code Reasoning Writing with AI Anomaly Detection
Recent News

🎉 Three papers accepted at EMNLP 2026 Main conference!

➊ AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not ➋ PGMem: Tightly Coupled Persona–Memory Graph for Lifelong Personalized Agents and ➌ LLMs Learn Better In-Context from Rules than from Examples

💡 Check out our personalized memory paper “PGMem”

‘PGMem: Tightly Coupled Persona–Memory Graph for Lifelong Personalized Agents’ is now available on arXiv! Work with Wonjun Choi, Yerim Kim and Susik Yoon. Huge Congrats to Wonjun on his first-first author paper 😊🎉

💡 Check out our math reasoning paper "Cliff Token"

‘Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoning’ now available on arXiv! Work with Jaeyong Ko and Pilsung Kang. Congrats to Jaeyong on his very first paper 🎉

💡 Check out our implicit CoT paper "CIRF"

‘CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models’ now available on arXiv! Collaborated with HUFS team 🦉

Selected Publications

For an up-to-date list of publications, check out my Google Scholar.

* denotes equal contribution, † denotes equal contribution as senior role.

(2026). AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not. EMNLP 2026.
(2026). PGMem: Tightly Coupled Persona-Memory Graph for Lifelong Personalized Agents. EMNLP 2026.
(2026). Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams. KDD 2026.
(2026). RExBench: Can coding agents autonomously implement AI research extensions?. ACL 2026.
(2025). CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists. EMNLP 2025.